There are many routine decision tasks where a person receives input data from a computer and analyzes the data to come to a decision. In some cases, these decision tasks are multi-dimensional in input and cannot easily be grouped into a relatively small number of classes. Because the decisions cannot be so grouped, the decision process does not lend itself to automated or expert processes provided in traditional recognition or classification problems.
Further for many of these decision problems, it is difficult to measure the quality of each individual decision made by a person, but it is possible to measure the integral quality of a plurality of decisions as a whole made by the person over a period of time. This integral quality can be compared against other persons making similar decisions over a period of time and the relative expertise of each decision maker can be measured.
One example of routine decision tasks based on input data as discussed above is in retail goods allocation tasks. In such tasks a distribution or allocation expert reviews input data on a computer display screen a quantity of retail goods to be allocated in various quantities from warehouses to multiple retail stores selling the goods. Where there are 10 to 1000+ stores in the business and the quantity of goods to be allocated to each store varies from 0 to 100+, the number of possible allocation outcomes can easily exceed several thousand. Such a decision problem is so multi-dimensional it does not lend itself to automated solution based on recognition and classification systems.
Further, to measure the quality of an allocation by examining a specific allocation is not meaningful. For example, if a specific set of goods such as swimsuits is allocated to certain stores and turns out not to be profitable for those stores, this result may be due to weather conditions rather than lack of experience by the allocator. On the other hand, if over an entire season all the goods allocated by this same allocator generate the highest total profit or other metric, this same person might be recognized as an expert allocator. In other words, there is no absolutely right or wrong decision for each decision problem, but there are the best (expert) and the poorest decision makers.
Another problem in computerizing routine decision tasks of the above type is that best practices in the environment of the decision problem may change over time. For example, in the allocation of retail goods, business practices may change over time because of changes to the competitive environment or changes in the goals of the business entity.